全文共6821字，預計學習時長20分鐘

來源：Pexels

從自動駕駛汽車檢測路上的物體，到通過復雜的面部及身體語言識別發現可能的犯罪活動。多年來，研究人員一直在探索讓機器通過視覺識別物體的可能性。

這一特殊領域被稱為計算機視覺 (Computer Vision, CV)，在現代生活中有著廣泛的應用。

目標檢測 (ObjectDetection) 也是計算機視覺最酷的應用之一，這是不容置疑的事實。

現在的CV工具能夠輕松地將目標檢測應用于圖片甚至是直播視頻。本文將簡單地展示如何用TensorFlow創建實時目標檢測器。

建立一個簡單的目標檢測器

設置要求：

TensorFlow版本在1.15.0或以上

執行pip install TensorFlow安裝最新版本

一切就緒，現在開始吧！

設置環境

第一步：從Github上下載或復制TensorFlow目標檢測的代碼到本地計算機

在終端運行如下命令：

git clonehttps://github.com/tensorflow/models.git

第二步：安裝依賴項

下一步是確定計算機上配備了運行目標檢測器所需的庫和組件。

下面列舉了本項目所依賴的庫。（大部分依賴都是TensorFlow自帶的）

· Cython

· contextlib2

· pillow

· lxml

· matplotlib

若有遺漏的組件，在運行環境中執行pip install即可。

第三步：安裝Protobuf編譯器

谷歌的Protobuf，又稱Protocol buffers，是一種語言無關、平臺無關、可擴展的序列化結構數據的機制。Protobuf幫助程序員定義數據結構，輕松地在各種數據流中使用各種語言進行編寫和讀取結構數據。

Protobuf也是本項目的依賴之一。點擊這里了解更多關于Protobufs的知識。接下來把Protobuf安裝到計算機上。

打開終端或者打開命令提示符，將地址改為復制的代碼倉庫，在終端執行如下命令：

cd models/research \

wget -Oprotobuf.zip https://github.com/protocolbuffers/protobuf/releases/download/v3.9.1/protoc-3.9.1-osx-x86_64.zip\

unzipprotobuf.zip

注意：請務必在models/research目錄解壓protobuf.zip文件。來源：Pexels

第四步：編輯Protobuf編譯器

從research/ directory目錄中執行如下命令編輯Protobuf編譯器：

./bin/protoc object_detection/protos/*.proto--python_out=.

用Python實現目標檢測

現在所有的依賴項都已經安裝完畢，可以用Python實現目標檢測了。

在下載的代碼倉庫中，將目錄更改為：

models/research/object_detection

這個目錄下有一個叫object_detection_tutorial.ipynb的ipython notebook。該文件是演示目標檢測算法的demo，在執行時會用到指定的模型：

ssd_mobilenet_v1_coco_2017_11_17

這一測試會識別代碼庫中提供的兩張測試圖片。下面是測試結果之一：

要檢測直播視頻中的目標還需要一些微調。在同一文件夾中新建一個Jupyter notebook，按照下面的代碼操作：

[1]:

import numpy as np

import os

import six.moves.urllib as urllib

import sys

import tarfile

import tensorflow as tf

import zipfile

from distutils.version import StrictVersion

from collections import defaultdict

from io import StringIO

from matplotlib import pyplot as plt

from PIL import Image

# This isneeded since the notebook is stored in the object_detection folder.

sys.path.append("..")

from utils import ops as utils_ops

if StrictVersion(tf.__version__) < StrictVersion('1.12.0'):

raise ImportError('Please upgrade your TensorFlow installation to v1.12.*.')

[2]:

# This isneeded to display the images.

get_ipython().run_line_magic('matplotlib', 'inline')

[3]:

# Objectdetection imports

# Here arethe imports from the object detection module.

from utils import label_map_util

from utils import visualization_utils as vis_util

[4]:

# Modelpreparation

# Anymodel exported using the `export_inference_graph.py` tool can be loaded heresimply by changing `PATH_TO_FROZEN_GRAPH` to point to a new .pb file.

# Bydefault we use an "SSD with Mobilenet" model here.

#See https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md

#for alist of other models that can be run out-of-the-box with varying speeds andaccuracies.

# Whatmodel to download.

MODEL_NAME= 'ssd_mobilenet_v1_coco_2017_11_17'

MODEL_FILE= MODEL_NAME + '.tar.gz'

DOWNLOAD_BASE= 'http://download.tensorflow.org/models/object_detection/'

# Path tofrozen detection graph. This is the actual model that is used for the objectdetection.

PATH_TO_FROZEN_GRAPH= MODEL_NAME + '/frozen_inference_graph.pb'

# List ofthe strings that is used to add correct label for each box.

PATH_TO_LABELS= os.path.join('data', 'mscoco_label_map.pbtxt')

[5]:

#DownloadModel

opener =urllib.request.URLopener()

opener.retrieve(DOWNLOAD_BASE+ MODEL_FILE, MODEL_FILE)

tar_file =tarfile.open(MODEL_FILE)

for file in tar_file.getmembers():

file_name= os.path.basename(file.name)

if'frozen_inference_graph.pb'in file_name:

tar_file.extract(file,os.getcwd())

[6]:

# Load a(frozen) Tensorflow model into memory.

detection_graph= tf.Graph()

with detection_graph.as_default():

od_graph_def= tf.GraphDef()

withtf.gfile.GFile(PATH_TO_FROZEN_GRAPH, 'rb') as fid:

serialized_graph= fid.read()

od_graph_def.ParseFromString(serialized_graph)

tf.import_graph_def(od_graph_def,name='')

[7]:

# Loadinglabel map

# Labelmaps map indices to category names, so that when our convolution networkpredicts `5`,

#we knowthat this corresponds to `airplane`. Here we use internal utilityfunctions,

#butanything that returns a dictionary mapping integers to appropriate stringlabels would be fine

category_index= label_map_util.create_category_index_from_labelmap(PATH_TO_LABELS,use_display_name=True)

[8]:

defrun_inference_for_single_image(image, graph):

with graph.as_default():

with tf.Session() as sess:

# Get handles to input and output tensors

ops= tf.get_default_graph().get_operations()

all_tensor_names= {output.name for op in ops for output in op.outputs}

tensor_dict= {}

for key in [

'num_detections', 'detection_boxes', 'detection_scores',

'detection_classes', 'detection_masks']:

tensor_name= key + ':0'

if tensor_name in all_tensor_names:

tensor_dict[key]= tf.get_default_graph().get_tensor_by_name(tensor_name)

if'detection_masks'in tensor_dict:

# The following processing is only for single image

detection_boxes= tf.squeeze(tensor_dict['detection_boxes'], [0])

detection_masks= tf.squeeze(tensor_dict['detection_masks'], [0])

# Reframe is required to translate mask from boxcoordinates to image coordinates and fit the image size.

real_num_detection= tf.cast(tensor_dict['num_detections'][0], tf.int32)

detection_boxes= tf.slice(detection_boxes, [0, 0], [real_num_detection, -1])

detection_masks= tf.slice(detection_masks, [0, 0, 0], [real_num_detection, -1, -1])

detection_masks_reframed= utils_ops.reframe_box_masks_to_image_masks(

detection_masks,detection_boxes, image.shape[1],image.shape[2])

detection_masks_reframed= tf.cast(

tf.greater(detection_masks_reframed,0.5),tf.uint8)

# Follow the convention by adding back the batchdimension

tensor_dict['detection_masks'] =tf.expand_dims(

detection_masks_reframed,0)

image_tensor= tf.get_default_graph().get_tensor_by_name('image_tensor:0')

# Run inference

output_dict= sess.run(tensor_dict, feed_dict={image_tensor: image})

# all outputs are float32 numpy arrays, so convert typesas appropriate

output_dict['num_detections'] =int(output_dict['num_detections'][0])

output_dict['detection_classes'] =output_dict[

'detection_classes'][0].astype(np.int64)

output_dict['detection_boxes'] =output_dict['detection_boxes'][0]

output_dict['detection_scores'] =output_dict['detection_scores'][0]

if'detection_masks'in output_dict:

output_dict['detection_masks'] =output_dict['detection_masks'][0]

return output_dict

[9]:

import cv2

cam =cv2.cv2.VideoCapture(0)

rolling = True

while (rolling):

ret,image_np = cam.read()

image_np_expanded= np.expand_dims(image_np, axis=0)

# Actual detection.

output_dict= run_inference_for_single_image(image_np_expanded, detection_graph)

# Visualization of the results of a detection.

vis_util.visualize_boxes_and_labels_on_image_array(

image_np,

output_dict['detection_boxes'],

output_dict['detection_classes'],

output_dict['detection_scores'],

category_index,

instance_masks=output_dict.get('detection_masks'),

use_normalized_coordinates=True,

line_thickness=8)

cv2.imshow('image', cv2.resize(image_np,(1000,800)))

if cv2.waitKey(25) & 0xFF == ord('q'):

break

cv2.destroyAllWindows()

cam.release()

在運行Jupyter notebook時，網絡攝影系統會開啟并檢測所有原始模型訓練過的物品類別。來源：Pexels

感謝閱讀本文，如果有什么建議，歡迎在評論區積極發言喲~

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